experiments for running DiskANN & BM25 on Arch 4090

benchmarks/bm25_diskann_baselines/run_diskann.py

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+#!/usr/bin/env python3
+"""
+Run DiskANN with real NQ queries (search-only timing).
+
+Steps:
+- Load queries from nq_open.jsonl
+- Compute embeddings (facebook/contriever-msmarco) once upfront
+- Search via DiskANN (no recompute, no batching), measure per-query latency
+
+Example:
+  python benchmarks/bm25_diskann_baselines/run_diskann_nq.py \
+    --index-dir benchmarks/data/indices/diskann_rpj_wiki \
+    --index-prefix ann \
+    --queries-file benchmarks/data/queries/nq_open.jsonl \
+    --num-queries 200 --top-k 10 --complexity 120 --threads 1 --beam-width 1
+"""
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+
+
+def load_queries(path: Path, limit: int | None) -> list[str]:
+    out: list[str] = []
+    with open(path, encoding="utf-8") as f:
+        for line in f:
+            obj = json.loads(line)
+            out.append(obj["query"])
+            if limit and len(out) >= limit:
+                break
+    return out
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(
+        description="DiskANN baseline on real NQ queries (search-only timing)"
+    )
+    ap.add_argument(
+        "--index-dir",
+        default="benchmarks/data/indices/diskann_rpj_wiki",
+        help="Directory containing DiskANN files",
+    )
+    ap.add_argument("--index-prefix", default="ann")
+    ap.add_argument("--queries-file", default="benchmarks/data/queries/nq_open.jsonl")
+    ap.add_argument("--num-queries", type=int, default=200)
+    ap.add_argument("--top-k", type=int, default=10)
+    ap.add_argument("--complexity", type=int, default=120)
+    ap.add_argument("--threads", type=int, default=1)
+    ap.add_argument("--beam-width", type=int, default=1)
+    ap.add_argument("--cache-mechanism", type=int, default=2)
+    ap.add_argument("--num-nodes-to-cache", type=int, default=0)
+    args = ap.parse_args()
+
+    index_dir = Path(args.index_dir).resolve()
+    if not index_dir.is_dir():
+        raise SystemExit(f"Index dir not found: {index_dir}")
+
+    qpath = Path(args.queries_file).resolve()
+    if not qpath.exists():
+        raise SystemExit(f"Queries file not found: {qpath}")
+
+    queries = load_queries(qpath, args.num_queries)
+    print(f"Loaded {len(queries)} queries from {qpath}")
+
+    # Compute embeddings once (exclude from timing)
+    from leann.api import compute_embeddings as _compute
+
+    embs = _compute(
+        queries,
+        model_name="facebook/contriever-msmarco",
+        mode="sentence-transformers",
+        use_server=False,
+    ).astype(np.float32)
+    if embs.ndim != 2:
+        raise SystemExit("Embedding compute failed or returned wrong shape")
+
+    # Build searcher
+    from leann_backend_diskann.diskann_backend import DiskannSearcher as _DiskannSearcher
+
+    index_prefix_path = str(index_dir / args.index_prefix)
+    searcher = _DiskannSearcher(
+        index_prefix_path,
+        num_threads=int(args.threads),
+        cache_mechanism=int(args.cache_mechanism),
+        num_nodes_to_cache=int(args.num_nodes_to_cache),
+    )
+
+    # Warmup (not timed)
+    _ = searcher.search(
+        embs[0:1],
+        top_k=args.top_k,
+        complexity=args.complexity,
+        beam_width=args.beam_width,
+        prune_ratio=0.0,
+        recompute_embeddings=False,
+        batch_recompute=False,
+        dedup_node_dis=False,
+    )
+
+    # Timed loop
+    times: list[float] = []
+    for i in range(embs.shape[0]):
+        t0 = time.time()
+        _ = searcher.search(
+            embs[i : i + 1],
+            top_k=args.top_k,
+            complexity=args.complexity,
+            beam_width=args.beam_width,
+            prune_ratio=0.0,
+            recompute_embeddings=False,
+            batch_recompute=False,
+            dedup_node_dis=False,
+        )
+        times.append(time.time() - t0)
+
+    times_sorted = sorted(times)
+    avg = float(sum(times) / len(times))
+    p50 = times_sorted[len(times) // 2]
+    p95 = times_sorted[max(0, int(len(times) * 0.95) - 1)]
+
+    print("\nDiskANN (NQ, search-only) Report")
+    print(f"  queries: {len(times)}")
+    print(
+        f"  k: {args.top_k}, complexity: {args.complexity}, beam_width: {args.beam_width}, threads: {args.threads}"
+    )
+    print(f"  avg per query: {avg:.6f} s")
+    print(f"  p50/p95: {p50:.6f}/{p95:.6f} s")
+    print(f"  QPS: {1.0 / avg:.2f}")
+
+
+if __name__ == "__main__":
+    main()